Monitoring cardiac events is of clinical importance in the early detection of potentially fatal conditions. Current technologies involve contact sensors the individual must wear constantly. Such a requirement can lead to patient discomfort, dependency, loss of dignity, and further may fail due to a variety of reasons including refusal to wear the monitoring device. Elderly cardiac patients are even more likely to suffer from the adverse effects of continued monitoring.
Among many cardiac diseases involving rhythmic disorders, atrial fibrillation (A-fib) represents ⅓ of hospital admissions for cardiac issues. A-fib can cause palpitations, fainting, chest pain, or congestive heart failure and even stroke. It is one of the most common sustained arrhythmias. It increases with age and presents with a wide spectrum of symptoms and severity. There are over 2 million Americans diagnosed with A-fib and is most frequent in elderly patients. Unobtrusive, non-contact, imaging based methods are needed for monitoring cardiac patients for A-fib episodes.
Accordingly, what is needed in this art are sophisticated systems and methods for processing a time-series signal generated by video images captured of a subject of interest in a non-contact, remote sensing environment such that the existence of a cardiac arrhythmia can be determined for that subject.